Book Image

Deep Learning for Beginners

By : Dr. Pablo Rivas
Book Image

Deep Learning for Beginners

By: Dr. Pablo Rivas

Overview of this book

With information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and you already have the basic mathematical and programming knowledge required to get started. The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples, and learn how to build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book. By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks.

Related Content you might be interested in

Current Title:

Small book image
Deep Learning for Beginners
Dr. Pablo Rivas
Sep, 2020 | 432 pages
Small book image
Deep Learning with Hadoop
Dipayan Dev
Feb, 2017 | 206 pages
Small book image
Hands-On Neural Networks with Keras
Niloy Purkait
Mar, 2019 | 462 pages
Small book image
Hands-On Deep Learning Architectures with Python
Yuxi Hayden Liu | Saransh Mehta
Apr, 2019 | 316 pages
Table of Contents (20 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Getting Up to Speed
Section 1: Getting Up to Speed
Free Chapter
2
Introduction to Machine Learning
Introduction to Machine Learning
Diving into the ML ecosystem
Training ML algorithms from data
Introducing deep learning
Why is deep learning important today?
Summary
Questions and answers
References
3
Setup and Introduction to Deep Learning Frameworks
Setup and Introduction to Deep Learning Frameworks
Introduction to Colaboratory
Introduction and setup of TensorFlow
Introduction and setup of Keras
Introduction to PyTorch
Introduction to Dopamine
Other deep learning libraries
Summary
Questions and answers
References
4
Preparing Data
Preparing Data
Binary data and binary classification
Categorical data and multiple classes
Real-valued data and univariate regression
Altering the distribution of data
Data augmentation
Data dimensionality reduction
Ethical implications of manipulating data
Summary
Questions and answers
References
5
Learning from Data
Learning from Data
Learning for a purpose
Measuring success and error
Identifying overfitting and generalization
The art behind learning
Ethical implications of training deep learning algorithms
Summary
Questions and answers
References
6
Training a Single Neuron
Training a Single Neuron
The perceptron model
The perceptron learning algorithm
A perceptron over non-linearly separable data
Summary
Questions and answers
References
7
Training Multiple Layers of Neurons
Training Multiple Layers of Neurons
The MLP model
Minimizing the error
Finding the best hyperparameters
Summary
Questions and answers
References
8
Section 2: Unsupervised Deep Learning
Section 2: Unsupervised Deep Learning
9
Autoencoders
Autoencoders
Introduction to unsupervised learning
Encoding and decoding layers
Applications in dimensionality reduction and visualization
Ethical implications of unsupervised learning
Summary
Questions and answers
References
10
Deep Autoencoders
Deep Autoencoders
Introducing deep belief networks
Making deep autoencoders
Exploring latent spaces with deep autoencoders
Summary
Questions and answers
References
11
Variational Autoencoders
Variational Autoencoders
Introducing deep generative models
Examining the VAE model
Comparing a deep and shallow VAE on MNIST
Thinking about the ethical implications of generative models
Summary
Questions and answers
References
12
Restricted Boltzmann Machines
Restricted Boltzmann Machines
Introduction to RBMs
Learning data representations with RBMs
Comparing RBMs and AEs
Summary
Questions and answers
References
13
Section 3: Supervised Deep Learning
Section 3: Supervised Deep Learning
14
Deep and Wide Neural Networks
Deep and Wide Neural Networks
Wide neural networks
Dense deep neural networks
Sparse deep neural networks
Hyperparameter optimization
Summary
Questions and answers
References
15
Convolutional Neural Networks
Convolutional Neural Networks
Introduction to convolutional neural networks
Convolution in n-dimensions
Convolutional layers
Pooling strategies
Convolutional neural network for CIFAR-10
Summary
Questions and answers
References
16
Recurrent Neural Networks
Recurrent Neural Networks
Introduction to recurrent neural networks
Long short-term memory models
Sequence-to-vector models
Vector-to-sequence models
Sequence-to-sequence models
Ethical implications
Summary
Questions and answers
References
17
Generative Adversarial Networks
Generative Adversarial Networks
Introducing adversarial learning
Training a GAN
Comparing GANs and VAEs
Thinking about the ethical implications of GANs
Summary
Questions and answers
References
18
Final Remarks on the Future of Deep Learning
Final Remarks on the Future of Deep Learning
Looking for advanced topics in deep learning
Learning with more resources from Packt
Summary
References
19
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Who this book is for

This book is for aspiring data scientists and deep learning engineers who want to get started with the absolute fundamentals of deep learning and neural networks. Now, about requirements:

  • No prior exposure to deep learning or machine learning is necessary, but it would be a plus.
  • Some familiarity with linear algebra and Python programming is all you need to get started.

This book is for people who value their time and want to get to the point and learn the deep learning recipes needed to do things.

Deep learning can be intimidating if you don’t know the basics. Many people are discouraged because they cannot follow the terminology or sample programs they see on the web. This causes people to make poor decisions about the selection of deep learning algorithms and renders them unable to foresee the consequences of such choices. Therefore, this book is for people who do the following:

  • Value access to good definitions of deep learning concepts
  • Want a structured method to learn deep learning from scratch
  • Desire to know the fundamental concepts and really understand them
  • Want to know how to preprocess data for usage in deep learning algorithms
  • Are curious about some advanced deep learning algorithms

For details about the contents of each chapter, read the next section.

Previous Section
Next Section